
'''simple docstring'''


import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def 			__snake_case  (     _UpperCAmelCase       :							Union[str, Any]):
   UpperCamelCase	  =	checkpoints.load_tax_checkpoint(a__)
   UpperCamelCase	  =	flatten_dict(a__)
   return flax_params



def 			__snake_case  (     _UpperCAmelCase       :							List[str]):
   UpperCamelCase	  =	{}

   UpperCamelCase	  =	{
       '''token_embedder''': '''embeddings''',
       '''encoder_norm''': '''layernorm''',
       '''kernel''': '''weight''',
       '''.out''': '''.output''',
       '''scale''': '''weight''',
       '''embedders_0.pos_embedding''': '''row_embedder.weight''',
       '''embedders_1.pos_embedding''': '''column_embedder.weight''',
   }

   UpperCamelCase	  =	{
       '''query''': '''attention.query''',
       '''key''': '''attention.key''',
       '''value''': '''attention.value''',
       '''output.dense''': '''output''',
       '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
       '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
       '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
       '''mlp.''': '''mlp.DenseReluDense.''',
       '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
       '''self_attention.o''': '''self_attention.attention.o''',
       '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
       '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
       '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
       '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
   }

   for key in flax_dict.keys():
      if "target" in key:
         # remove the first prefix from the key
         UpperCamelCase	  =	'''.'''.join(key[1:])

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            UpperCamelCase	  =	new_key.replace(a__,   a__)

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               UpperCamelCase	  =	new_key.replace(a__,   a__)

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            UpperCamelCase	  =	re.sub(R'''layers_(\d+)''',   R'''layer.\1''',   a__)
            UpperCamelCase	  =	new_key.replace('''encoder''',   '''encoder.encoder''')

         elif "layers" in new_key and "decoder" in new_key:
            # use regex to replace the layer number
            UpperCamelCase	  =	re.sub(R'''layers_(\d+)''',   R'''layer.\1''',   a__)

         UpperCamelCase	  =	flax_dict[key]

   UpperCamelCase	  =	{}
   # convert converted_dict into torch format
   for key in converted_dict.keys():
      if ("embed_tokens" not in key) and ("embedder" not in key):
         UpperCamelCase	  =	torch.from_numpy(converted_dict[key].T)
      else:
         UpperCamelCase	  =	torch.from_numpy(converted_dict[key])

   return converted_torch_dict



def 			__snake_case  (     _UpperCAmelCase       :							Tuple,   _UpperCAmelCase       :							Optional[int],   _UpperCAmelCase       :							List[str]=False,   _UpperCAmelCase       :							Optional[Any]=False):
   UpperCamelCase	  =	get_flax_param(a__)

   if not use_large:
      UpperCamelCase	  =	PixaStructVisionConfig()
      UpperCamelCase	  =	PixaStructTextConfig()
   else:
      UpperCamelCase	  =	PixaStructVisionConfig(
          hidden_size=1536,   d_ff=3968,   num_attention_heads=24,   num_hidden_layers=18)
      UpperCamelCase	  =	PixaStructTextConfig(hidden_size=1536,   d_ff=3968,   num_heads=24,   num_layers=18)
   UpperCamelCase	  =	PixaStructConfig(
       vision_config=encoder_config.to_dict(),   text_config=decoder_config.to_dict(),   is_vqa=a__)

   UpperCamelCase	  =	PixaStructForConditionalGeneration(a__)

   UpperCamelCase	  =	rename_and_convert_flax_params(a__)
   model.load_state_dict(a__)

   UpperCamelCase	  =	AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''')
   UpperCamelCase	  =	PixaStructImageProcessor()
   UpperCamelCase	  =	PixaStructProcessor(image_processor=a__,   tokenizer=a__)

   if use_large:
      UpperCamelCase	  =	4096

   UpperCamelCase	  =	True

   # mkdir if needed
   os.makedirs(a__,   exist_ok=a__)

   model.save_pretrained(a__)
   processor.save_pretrained(a__)

   print('''Model saved in {}'''.format(a__))


if __name__ == "__main__":
   snake_case_			:	str 	=       argparse.ArgumentParser()
   parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--use_large', action='store_true', help='Use large model.')
   parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
   snake_case_			:	str 	=       parser.parse_args()

   convert_pixastruct_original_pytorch_checkpoint_to_hf(
       args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
   )






import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax

   from transformers.models.big_bird.modeling_flax_big_bird import (
       FlaxBigBirdForCausalLM,
       FlaxBigBirdForMaskedLM,
       FlaxBigBirdForMultipleChoice,
       FlaxBigBirdForPreTraining,
       FlaxBigBirdForQuestionAnswering,
       FlaxBigBirdForSequenceClassification,
       FlaxBigBirdForTokenClassification,
       FlaxBigBirdModel,
   )




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def __init__(		self				:   List[Any]							, lowercase_				:   Union[str, Any]							, lowercase_				:   Union[str, Any]=2							, lowercase_				:   Dict=56							, lowercase_				:   str=True							, lowercase_				:   Optional[Any]=True							, lowercase_				:   List[Any]=True							, lowercase_				:   Union[str, Any]=True							, lowercase_				:   Any=99							, lowercase_				:   Optional[int]=32							, lowercase_				:   Tuple=2							, lowercase_				:   int=2							, lowercase_				:   List[str]=7							, lowercase_				:   Any="gelu_new"							, lowercase_				:   List[str]=0.1							, lowercase_				:   str=0.1							, lowercase_				:   List[Any]=512							, lowercase_				:   List[str]=16							, lowercase_				:   Optional[int]=2							, lowercase_				:   Union[str, Any]=0.02							, lowercase_				:   Union[str, Any]=4							, lowercase_				:   Union[str, Any]="block_sparse"							, lowercase_				:   Tuple=True							, lowercase_				:   Dict=False							, lowercase_				:   Dict=2							, lowercase_				:   Dict=3							, )						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    parent
   _UpperCamelCase           =    batch_size
   _UpperCamelCase           =    seq_length
   _UpperCamelCase           =    is_training
   _UpperCamelCase           =    use_attention_mask
   _UpperCamelCase           =    use_token_type_ids
   _UpperCamelCase           =    use_labels
   _UpperCamelCase           =    vocab_size
   _UpperCamelCase           =    hidden_size
   _UpperCamelCase           =    num_hidden_layers
   _UpperCamelCase           =    num_attention_heads
   _UpperCamelCase           =    intermediate_size
   _UpperCamelCase           =    hidden_act
   _UpperCamelCase           =    hidden_dropout_prob
   _UpperCamelCase           =    attention_probs_dropout_prob
   _UpperCamelCase           =    max_position_embeddings
   _UpperCamelCase           =    type_vocab_size
   _UpperCamelCase           =    type_sequence_label_size
   _UpperCamelCase           =    initializer_range
   _UpperCamelCase           =    num_choices

   _UpperCamelCase           =    rescale_embeddings
   _UpperCamelCase           =    attention_type
   _UpperCamelCase           =    use_bias
   _UpperCamelCase           =    block_size
   _UpperCamelCase           =    num_random_blocks







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size)

   _UpperCamelCase           =    None
   if self.use_attention_mask:
     _UpperCamelCase           =    random_attention_mask([self.batch_size, self.seq_length])

   _UpperCamelCase           =    None
   if self.use_token_type_ids:
     _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size)

   _UpperCamelCase           =    BigBirdConfig(
       vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, is_decoder=lowercase_							, initializer_range=self.initializer_range							, attention_type=self.attention_type							, block_size=self.block_size							, num_random_blocks=self.num_random_blocks							, use_bias=self.use_bias							, rescale_embeddings=self.rescale_embeddings							, )

   return config, input_ids, token_type_ids, attention_mask







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.prepare_config_and_inputs()
   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    config_and_inputs
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "token_type_ids": token_type_ids,
       "attention_mask": attention_mask,
   }
   return config, inputs_dict




@require_flax
class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					(
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 __A			       =					False
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    FlaxBigBirdModelTester(self)







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	List[str]:


   """simple docstring"""







   super().test_from_pretrained_save_pretrained()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Any)						->	int:


   """simple docstring"""







   super().test_from_pretrained_with_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   str)						->	Union[str, Any]:


   """simple docstring"""







   super().test_hidden_states_output()







 @slow
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Optional[int]:


   """simple docstring"""







   for model_class_name in self.all_model_classes:
     _UpperCamelCase           =    model_class_name.from_pretrained("google/bigbird-roberta-base")
     self.assertIsNotNone(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	str:


   """simple docstring"""







   if self.test_attn_probs:
     super().test_attention_outputs()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Dict:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       _UpperCamelCase           =    self._prepare_for_class(lowercase_							, lowercase_)
       _UpperCamelCase           =    model_class(lowercase_)

       @jax.jit
       def model_jitted(lowercase_				:   Dict							, lowercase_				:   List[Any]=None							, **lowercase_				:   Tuple):
         return model(input_ids=lowercase_							, attention_mask=lowercase_							, **lowercase_)

       with self.subTest("JIT Enabled"):
         _UpperCamelCase           =    model_jitted(**lowercase_).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           _UpperCamelCase           =    model_jitted(**lowercase_).to_tuple()

       self.assertEqual(len(lowercase_)							, len(lowercase_))
       for jitted_output, output in zip(lowercase_							, lowercase_):
         self.assertEqual(jitted_output.shape							, output.shape)







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   List[str]							, lowercase_				:   List[str]							, lowercase_				:   List[str]							, lowercase_				:   str=1e-5							, lowercase_				:   int="outputs"							, lowercase_				:   List[str]=None)						->	Tuple:


   """simple docstring"""







   if name.startswith("outputs.attentions"):
     return
   else:
     super().check_pt_flax_outputs(lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_)




